Pipeline Inspection Gauge Research Articles

A Crack Detection Method for Pipelines Using Wavelet-Based Decision-Level Data Fusion

A novel crack detection method using wavelet-based decision-level data fusion is proposed and verified by simulations and experiments. In this work, we established a crack signal model using wavelet functions that fits all crack signal scales without need of windowing, and showed that the signal-to-noise-ratio (SNR) difference between the independent signals to be fused has a significant effect on the overall detection performance. Based on this observation, we designed four wavelet-based decision-level data fusion rules. We then presented a detection method where wavelet processing results of individual NDT input are accepted or rejected based on said rules to produce optimized estimation accuracy. To evaluate the proposed method, the first group of simulations were implemented to show the proposed method identifies the inner and outer-surface cracks with good estimation accuracy; the second and third groups of experiments showed the proposed method does improve upon individual detection methods alone, and has better detection performance than three state-of-the-art methods; finally, the pull-rig experiments verified on our own pipeline inspection gauge (PIG) that the proposed method does improve the detection probability beyond that of individual detection methods, in an actual high-speed pipeline inline inspection (ILI).

Velocity Prediction of a Pipeline Inspection Gauge (PIG) with Machine Learning

A device known as a pipeline inspection gauge (PIG) runs through oil and gas pipelines which performs various maintenance operations in the oil and gas industry. The PIG velocity, which plays a role in the efficiency of these operations, is usually determined indirectly from odometers installed in it. Although this is a relatively simple technique, the loss of contact between the odometer wheel and the pipeline results in measurement errors. To help reduce these errors, this investigation employed neural networks to estimate the speed of a prototype PIG, using the pressure difference that acts on the device inside the pipeline and its acceleration instead of using odometers. Static networks (e.g., multilayer perceptron) and recurrent networks (e.g., long short-term memory) were built, and in addition, a prototype PIG was developed with an embedded system based on Raspberry Pi 3 to collect speed, acceleration and pressure data for the model training. The implementation of the supervised neural networks used the Python library TensorFlow package. To train and evaluate the models, we used the PIG testing pipeline facilities available at the Petroleum Evaluation and Measurement Laboratory of the Federal University of Rio Grande do Norte (LAMP/UFRN). The results showed that the models were able to learn the relationship among the differential pressure, acceleration and speed of the PIG. The proposed approach can complement odometer-based systems, increasing the reliability of speed measurements.

A novel study on bypass module in self-regulated pipeline inspection gauge to enhance anti-blocking capability for secure and efficient natural gas transportation

Bypass pigging technology is an emerging strategy with promising potential to reduce the velocity of pipeline inspection gauge (PIG) and mitigate pigging-induced slug volume for oil and gas transportation systems. Nonetheless, the critical issue of bypass pigs being blocked in pipelines is a major concern for wide implementations of this new technology. To this end, this study newly proposes an intelligent self-regulated bypass pig prototype by developing an internal bypass regulating module to enhance the anti-blocking capability for pigging operations. To facilitate the optimal design of the bypass regulating module, force variation characteristics of the bypass valve in blocked bypass pigs are of great significance. Accordingly, this study thoroughly investigates bypass valve forces for bypass pigging under the blockage status both experimentally and numerically. The experimental results show that an increase in gas velocity can almost linearly increase the valve force, which is mainly affected by the driving gas flow rate. Specifically, when the gas velocity increases from 1.26 to 4.4 m/s, the valve force can be increased from 1.46 to 12.88 N on average. In addition, a CFD-based numerical model was developed and experimentally validated to calculate valve forces. The numerical model, which has the mean bias error below −0.886 N with the index of agreement over 0.98, can be used as an effective approach to valve force analyses. Finally, the optimal design scheme for bypass pigging with anti-blocking capability was proposed, which can considerably facilitate the secure and efficient pigging performance and natural gas transportation.

Pipeline inspection using a low-cost Wi-Fi based intelligent pigging solution

This study presents a low-cost smart pipeline inspection gauge (PIG) designed for pipeline defect (leakage) detection, and for quick data access and recovery for the purpose of analysis, utilizing locally sourced materials and off-the-shelf sensors and electronics. The PIG’s electronic circuit is designed to house the sensors and allow for easy reception and transfer of pressure measurements to the pipeline manager’s laptop via a WiFi module. A pressure sensor, a motion sensor, a wireless communicator, and an Arduino Microcontroller are utilized in the development of this PIG. The PIG was tested experimentally by being put through stationary, no load-no defect, and no load-defect tests respectively. The PIG was kept still during the stationary tests but was conveyed within a 160 mm diameter Polyvinyl Chloride (PVC) conduit with a length of 6.7 m for both the no load-no defect and no load-defect tests, using a 0.125 Hp direct current (DC) motor, with a gearbox attachment to pull from one end to the other. Using a WiFi module and the PuTTY program, the pressure values were retrieved. The test results revealed that P1 (front pressure) values were higher than P2 (rear pressure) values for the no load tests. P1 readings ranged from 1213 to 1214 Pa, with an average of 1213.86 Pa for the no load-no defect tests. The average P2 value was 1094.24 Pa, with a range of 1094.24 Pa to 1094.75 Pa. The pressure values for the no load-defect cases began at 1226.8 Pa and steadily decreased for the first 1.5 minutes, then remained at an average of 1214.2 Pa for the next 20 seconds until they arrived at the first defected point, where a value of 1216.1 Pa was recorded. The PIG traversed the pipeline until it had caught all of the pressure pulses at the defective sites. The higher pressure pulses (spikes) observed at the points of defects created along the pipeline in the experimental results from the no load-defect tests indicates that the Smart PIG was capable of detecting the created defects and demonstrated that the low-cost Smart PIG can be used to detect leakages on a pipe and can also be deployed in real life situations.

Experimental evaluation of multi-jointed soft detection arm of geometry inspection gauges for detecting internal convex defects in natural gas pipelines

Regular deformation inspection of in-service natural gas pipelines can reduce accidents caused by pipeline failure. In this study, a novel contact-testing geometry inspection gauge is proposed, and its multi-jointed soft detection arm is the focus of our study. 3D printing technology and mold casting were used to fabricate a newly designed detection arm. A finite element method was used to analyze the process of the detection arm passing through the convex defect and optimize its joint number. Then, a series of comparison experiments were conducted with various convex defects to evaluate the superiority of the measuring performance of the multi-jointed soft detection arm. The experimental results show that compared to the no-jointed detection arm, the multi-jointed detection arm has a more accurate experimental value, especially for defects with closer heights (8 and 10 mm) and is relatively more stable in repeated experiments. This study provides options for the design of a flexible caliper pipeline inspection gauge for deformation inspection in pipelines.

Motion Law and Mechanical Properties of PIGs When Passing through a Pipe Bend

Pipeline inspection gauges (PIGs), as a kind of pipeline robot, are very efficient tools for cleaning and inspecting pipelines. However, the occurrence of obstructions in PIGs has always been a problem. The main cause of the PIG clogging pipeline problem is the reduced pressure differential between the front and rear due to damage to the cup. In this paper, a rigid-flexible coupled multibody dynamic motion system is established by importing flexible bodies. The stress and contact force generated by the elastic deformation of the cup in the pipe are analyzed. Moreover, the spacing ratio of PIG cups and the number of cups were changed, the number of cabin sections was increased, the bending of PIGs of different sizes and specifications was studied, and the influence of the cross-universal joint on the bending of PIGs, as well as the force between the cups and the core tube, was analyzed. Through the design and construction of the corresponding experimental equipment, the influence of the change in the number of leather cups on cornering is studied.

Benzene Exposure From Selected Work Tasks on Offshore Petroleum Installations on the Norwegian Continental Shelf, 2002-2018.

Work on offshore petroleum installations may cause exposure to benzene. Benzene is a carcinogenic agent, and exposure among workers should be as low as reasonably practicable. We aimed to assess short-term (less than 60 min) benzene exposure from the most frequent work tasks on offshore installations on the Norwegian continental shelf and identify determinants of exposure. In addition, we aimed to assess the time trend in task-based benzene measurements from 2002 to 2018. The study included 763 task-based measurements with a sampling duration of less than 60 min, collected on 28 offshore installations from 2002 to 2018. The measurements were categorized into 10 different tasks. Multilevel mixed-effect Tobit regression models were developed for two tasks: sampling and disassembling/assembling equipment. Benzene source, season, indoors or outdoors, design of process area, year of production start, sampling method, and work operation were considered as potential determinants for benzene exposure in the models. The overall geometric mean (GM) benzene exposure was 0.02 ppm (95% confidence intervals 95%(CI: 0.01-0.04). The pipeline inspection gauge (PIG) operation task was associated with the highest exposure, with a GM of 0.33 ppm, followed by work on flotation cells, disassembling/assembling, and sampling, with GMs of 0.16, 0.04, and 0.01 ppm, respectively. Significant determinants for the disassembling/assembling task were work operation (changing or recertifying valves, changing or cleaning filters, and breaking pipes) and benzene source. For sampling, the benzene source was a significant determinant. Overall, the task-based benzene exposure declined annually by 10.2% (CI 95%: -17.4 to -2.4%) from 2002 to 2018. The PIG operation task was associated with the highest exposure out of the ten tasks, followed by work on flotation cells and when performing disassembling/assembling of equipment. The exposure was associated with the type of benzene source that was worked on. Despite the decline in task-based exposure in 2002-2018, technical measures should still be considered in order to reduce the exposure.

Deformation and stress analysis of cup on pipeline inspection gauge based on reverse measurement

AbstractThe pipeline inspection gauge (PIG) is one of the most significant gauges in the field of oil–gas storage and transportation. It is essential to use PIG to clean and detect petroleum and natural gas pipelines. Predicting the deformation and stress distribution of the cup on the PIG is essential before pigging. To enhance these predictions, especially the deformation of the cup, a method for measuring the deformation of the cup based on reverse measurement was introduced and a novel finite element (FE) simulation model for investigation of the deformation of cups was proposed and verified in this paper. The point cloud data of the cup before and after entering the pipe can be obtained by the three‐dimensional (3D) laser scanner. Comparing the obtained point cloud data can measure the deformation of the cup. In addition, a 3D FE model was created to predict the deformation of the cup and the effective stress on the contact area. The result shows that there is a linear relationship between circumferential deformation and interference, while there is a nonlinear relationship between axial deformation, effective stress, and interference. These results provide a better understanding of the complex behavior of PIG during pigging.

Dynamic characteristics of the pipeline inspection gauge under girth weld excitation in submarine pipeline

Pipeline inner inspection technology based on Pipeline Inspection Gauge (PIG), is the primary means for ensuring the safety of submarine pipelines. The dynamic characteristics of a PIG can change abruptly with the excitation of obstacles such as girth welds inside the pipeline, which would result in failure or inaccuracy of the inspection results. This study establishes a dynamic model of the PIG sealing disc based on Kelvin spring damping in the circumferentially confined space. The axial vibration differential equations of the PIG is examined in detail. MSC/ADAMS is used to conduct the dynamic simulation of the PIG at different motion velocities and center of mass positions while passing through the girth weld process. Results indicate that the axial vibration caused by the girth weld intensifies substantially as the speed of the PIG increases, while the pitch and vertical vibrations exhibit a significant decline with an increase in the motion velocity. The change in the PIG’s center of mass positions has little effect on its axial vibration, while the pitch and vertical vibration conditions are significantly different in the same circumstances.

Application of Weld Scar Recognition in Small-Diameter Transportation Pipeline Positioning System

In order to improve the positioning accuracy of the pipeline inspection gauge (PIG), a pipeline positioning method, based on weld location, is proposed. The position of the welding scar is recognized by wavelet transform modulus maxima (WTMM). Equidistant welding scars provide positioning references to the strap-down inertial navigation system (SINS)/dead reckoning (DR) navigation system, which is the positioning algorithm in PIG. The following improvements have been made in relation to prior research. First, we suggest a selection strategy for the optimal mother wavelet and decomposition level; based on the strategy, WTMM can recognize the collision response between the PIG and submerged weld in the burst noise for the inertial measurement unit (IMU) output. Then, characteristic position (CP), which is the site of the weld scar, and non-holonomic constraints are utilized to decrease the position and the attitude error. By doing such, the SINS/DR/CP algorithm is proposed. The positioning error of the modified algorithm is 0.129% in the experiment, which performs better than other algorithms.

Mechanical behaviors of pipeline inspection gauge (pig) in launching process based on Coupled Eulerian-Lagrangian (CEL) method

Successful launching of the pig is the premise for ensuring the efficiency and accuracy of the pigging operation. This study investigates the effect of the flow field on pig launching by the three-dimensional (3D) fluid-structure interaction (FSI) model based on the Coupled Eulerian-Lagrangian (CEL) method for the first time. Besides, the flow field experiment and the dragging experiment are conducted to verify the reliability of the simulation results. Four parameters as friction force, mandrel flip angle, pig velocity, cup deformation are selected to describe the mechanical behaviors of the pig. Research results indicate that the flow field has a significant effect on contact behaviors between the pig and the pipeline in the pig running-in stage but less effect on that in the steady stage, which presents the necessity of the FSI simulation for investigating the pig launching process.

Speed excursion simulation of PIG using improved friction models

This study proposes two novel friction models of pipeline inspection gauges (PIGs) to simulate and predict speed excursions in long pipeline system pigging operations. Speed excursion of PIG indicates its sudden acceleration mainly caused by gas compressibility and frictional variation in low-pressure and low-flowrate gas pipelines. The first friction model adopts a dynamic friction table coupled with an exponential friction model to simulate the speed excursion caused by variations in friction. The second friction model utilizes a linear equation for friction variation caused by changes in wall thickness and pipe bends, then weight parameters are applied to determine the influence of each factor. These two friction models are tuning models based on field data to simulate speed excursions caused by frictional variation, which can be strategically selected according to the purpose of the simulation. In the numerical model, the transient gas flow equations are solved by the method of characteristics (MOC), and then the Runge–Kuta method is used to solve the dynamic equation of the PIGs. Simulation results applying the proposed friction models are compared to the field pigging data for three different routes operated by the Korea Gas Corporation (KOGAS), and the obtained simulation results are in good agreement with the field pigging data. The first model, tuned friction model, was able to simulate the average pigging velocity and speed excursions of the total distance ratio with high accuracy. The second proposed model, weighted friction model, was slightly less accurate than the first friction model, however it was able to predict the average pigging velocity and speed excursions under different operating conditions. These results mean that the speed excursion can be simulated and predicted with high accuracy using the proposed friction models through the tuning process. Therefore, these two novel friction models would provide insights for the operators to simulate and predict the unstable movement of the PIGs in their pipeline networks.

Pipeline Inspection Gauge Positioning System Based on Optical Fiber Distributed Acoustic Sensing

To maintain pipeline transportation’s optimal efficiency and safety, pipelines must be regularly inspected and cleaned by the pipeline inspection gauge (PIG). Since the PIG passes through the whole pipeline, which is generally tens of kilometers, observing where the PIG is working in real-time is necessary. When the PIG passes through the pipe joint, the vibration signal will generate by the collision between the PIG and the girth weld, which can transmit along the fiber cable besides the pipeline. In this paper, we demonstrate that the optical fiber distributed acoustic sensing (DAS) can acquire the collision vibration in terms of the V pattern in the waterfall diagram, whose vertex is the collision position of PIG. The vertex will not exist when the cable positions are far away from the pipeline, which is relatively common in practice. We proposed an algorithm based on the clustering algorithm and the Hough transform to detect the position of the PIG even when the vertex of the V pattern does not exist. A field experiment shows that the proposed positioning algorithms applied to the DAS can effectively track the PIG online.

Numerical investigation on sealing performance of drainage pipeline inspection gauge crossing pipeline elbows

AbstractA pipeline inspection gauge (PIG) is routinely passed throughout the long‐distance oil and gas pipelines by pipeline operators to clean the pipeline. Sealing performance is a significant evaluation index for PIG's safety operation. To comprehensively evaluate the PIG's seal performance, nonlinear finite element models were developed, and parametric analysis was conducted in this study. The accuracy of simulation model was validated from numerical and experimental results reported in the literature. The results show that comparing with the pigging in straight pipes, the sealing rubber cups of PIGs can be more easily detached from the pipe wall when passing through elbows, causing smaller sealing areas. For a typical elbow (with curvature radius equals to six times of pipe diameter) widely used in pipeline industry, the minimum sealing area of rubber cup is only 8.07% during common operation conditions. The sealing ability of rubber cups can be obviously enhanced by increasing the curvature radius for the pigging operation of small curvature elbow. Elbow radius slightly affects the cup's sealing behavior when the curvature radius is over six times of pipe's outer diameter. An increment in sealing cup interference can increase the contact area between cup and pipe wall, and the blockage risk of PIGs will be reduced due to the good sealing ability and sufficient driving force. The minimum interference required for the cups is 4% under a most common operation condition; that is, the sealing cup thickness, fluid pressure difference, and friction coefficient are 35 mm, 0.02 MPa, and 0.3, respectively. A proper decrease in sealing cup thickness will reduce the stiffness of rubber cups, indicating that the cups with a smaller thickness are more prone to deformation. Thus, an increase in differential pressure over PIG can enhance the sealing performance of cups when the cups are separated from pipe wall. A large friction coefficient is risky for a safe pigging due to the decrease in sealing region with the increase in friction coefficient. In engineering practice, proper measures should be taken to reduce friction force. Above all, the results obtained in this study provide a reference for the structural design of PIGs.

A Kriging-Based Magnetic Flux Leakage Method for Fast Defect Detection in Massive Pipelines

Abstract Systems in service continue to degrade with the passage of time. Pipelines are among the most common systems that wear away with usage. For public safety, it is of utmost importance to monitor pipelines. Magnetic flux leakage (MFL) testing is a widely used nondestructive evaluation (NDE) technique for defect detections within the pipelines, particularly those composed of ferromagnetic materials. Pipeline inspection gauge (PIG) procedure based on line scans can collect accurate MFL readings for defect detection. However, in real world, applications involving large pipe sectors such as extensive scanning techniques are extremely time consuming and costly. In this article, we develop a fast and cheap methodology that does not need MFL readings at all the points used in traditional PIG procedures but conducts defect detection with similar accuracy. We consider an under-sampling based scheme that collects MFL at uniformly chosen random scan points over large lattices instead of extensive PIG scans over all lattice points. On the basis of readings from the chosen random scan points, we use kriging to reconstruct MFL readings. Thereafter, we use thresholding-based segmentation on the reconstructed data for detecting defective areas. We demonstrate the applicability of our methodology on synthetic data generated using finite element models and on MFL data collected via laboratory experiments. In these experiments, spanning a wide range of defect types, our proposed novel MFL-based NDE methodology is witnessed to have operating characteristics within the acceptable threshold of PIG-based traditional methods and thus provide an extremely cost-effective, fast procedure with competing error rates.

Pipeline In-Line Inspection Method, Instrumentation and Data Management.

Pipelines play an important role in the national/international transportation of natural gas, petroleum products, and other energy resources. Pipelines are set up in different environments and consequently suffer various damage challenges, such as environmental electrochemical reaction, welding defects, and external force damage, etc. Defects like metal loss, pitting, and cracks destroy the pipeline’s integrity and cause serious safety issues. This should be prevented before it occurs to ensure the safe operation of the pipeline. In recent years, different non-destructive testing (NDT) methods have been developed for in-line pipeline inspection. These are magnetic flux leakage (MFL) testing, ultrasonic testing (UT), electromagnetic acoustic technology (EMAT), eddy current testing (EC). Single modality or different kinds of integrated NDT system named Pipeline Inspection Gauge (PIG) or un-piggable robotic inspection systems have been developed. Moreover, data management in conjunction with historic data for condition-based pipeline maintenance becomes important as well. In this study, various inspection methods in association with non-destructive testing are investigated. The state of the art of PIGs, un-piggable robots, as well as instrumental applications, are systematically compared. Furthermore, data models and management are utilized for defect quantification, classification, failure prediction and maintenance. Finally, the challenges, problems, and development trends of pipeline inspection as well as data management are derived and discussed.

Wear behaviors of polyurethane rubber used in pipeline inspection gauge

This study is concerned with the wear behaviors of the polyurethane rubber (UR) used in the pigging industry under the condition of interference fit. The pin-on-disc tribological tests were conducted to obtain the wear rate of the UR, and the wear mechanisms were identified by observing the worn surface via an optical microscope. Additionally, the relationship between the wear rate and the contact pressure was presented with the method of quadratic fitting.

Flow Characteristics of Disk Bypass Pipeline Inspection Gauge (PIG) in Natural Gas Pipelines using Computational Fluid Dynamics

Disk bypass pipeline inspection gauge (PIG) is considered as an efficient device for pigging operations including cleaning, maintaining and inspecting pipelines. The PIG performance is influenced by the fluid flow characteristics as PIG moves forward due to differential pressure of fluid around the PIG. This study focuses on flow characterization of fluid around disk bypass PIG for natural gases pipelines including methane, ethane, and butane using computational fluid dynamics approach. The control volume method with steady state Turbulent k-? model was applied for simulation purposes using ANSYS Fluent 19 software. Fluid velocities at different sections around PIG and differential pressure were investigated for various bypass opening percentages. The results showed that by increasing bypass opening percentages from 5% to 15%, fluid velocity at bypass opening section has reduced 28.28%, 40.43%, and 21.21% for ethane, butane, and methane, respectively, while differential pressure reduced 88%, 86% and 89%. This indicated that 15% bypass opening percentage provided the best flow characteristics among all cases considered. At 15% bypass opening percentage, methane resulted in the lowest fluid velocity at bypass opening section and lowest differential pressure compared to others. Additionally, a correlation of differential pressure of these gases as a function of bypass opening percentage and other parameters was also developed for first time. All results are important for design selection of PIG parameters for efficient pigging operation.

Experiment and simulation of a controllable multi-airbag sealing disc of pipeline inspection gauges (PIGs)

The use of Pipeline Inspection Gauges (PIGs) to clean the pipes and understand the exact location of existing pipes is essential to protect them from damage during operation. In-depth study of the most important component sealing disc is a new method to solve the failure of the current PIG in the working process. This study focuses on redesigning the sealing disc in combination with soft robot drive technology. First, the structure of a new multi-airbag sealing disc is composed of a conventional sealing disc and a plurality of airbags located above it. Then, the multi-airbag sealing disc was successfully fabricated by using 3D printing technology and multi-step molding, and finally the active control of the multi-airbag sealing disc was realized by inflating the airbag. For the newly designed multi-airbag sealing disc, the most important concern is the bending angle β of the sealing disc and the expansion ratio η of the airbag. Numerical and experimental results were compared to verify the accuracy of the numerical method. Based on this method, the effects of the position of the airbag, the height rate, and the thickness rate on the bending angle of the multi-airbag sealing disc and the expansion ratio of the airbag were studied. The results show that at a given air pressure, the position of the airbag does not substantially affect the expansion ratio of the airbag itself, and the smaller the distance between the airbag and the outer edge of the sealing disc, the easier it is to bend the sealing disc. The larger the height rate, the smaller the thickness rate, and the larger the bending angle of the sealing disc, the larger the expansion ratio of the airbag.

High-Precision and Four-Dimensional Tracking System with Dual Receivers of Pipeline Inspection Gauge

Pipeline inspection gauges (PIGs) are widely used for nondestructive testing of oil and natural gas pipelines, while above ground markers (AGMs) can locate and track the PIG through a variety of methods, including magnetic flux leakage signals, acoustic signals, and extremely low-frequency (ELF) magnetic signals. Traditional AGMs have the disadvantages of low positioning accuracy and only one-dimensional tracking capability. In this paper, a newly-designed PIG tracking system based on the ELF magnetic field is proposed by assembling dual receivers. Moreover, this paper develops a magnetic field sign-integration algorithm to achieve high-precision and four-dimensional (4-D) tracking of PIG. The simulation and experiment results demonstrate that the tracking system has the capability of 4-D tracking. In comparison with the previously published work, the designed tracking system improves the positioning accuracy and orientation tracking accuracy by more than 50%. The dual receivers tracking system also has the characteristic of high-robustness. Even in the state of lateral offset or tilt, it can still achieve accurate tracking of PIG. The realization of PIG’s high-precision 4-D tracking can improve the accuracy of defect location. Moreover, it can also provide the latest pipeline network layout and facilitate pipeline maintenance and pipeline surveying applications.